18 citations · 39 across the 6 of their papers we have counts for
6 papers
Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
Codefuse, Ling Team, : +30
Recent advancements in code large language models (LLMs) have demonstrated remarkable capabilities in code generation and understanding. It is still challenging to build a code LLM…
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
Zi Gong, Hang Yu, Cong Liao +3
Multi-task learning (MTL) benefits the fine-tuning of large language models (LLMs) by providing a single model with improved performance and generalization ability across tasks, pr…
SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy
Tingkai Zhang, Chaoyu Chen, Cong Liao +6
Text-to-SQL conversion is a critical innovation, simplifying the transition from complex SQL to intuitive natural language queries, especially significant given SQL's prevalence in…
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Bingchang Liu, Chaoyu Chen, Cong Liao +9
Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previo…
Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code
Ziyin Zhang, Chaoyu Chen, Bingchang Liu +5
In this work we systematically review the recent advancements in software engineering with language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 relate…
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Peng Di, Jianguo Li, Hang Yu +35
Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the…